




'''simple docstring'''







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_     						=				'''▁'''

lowerCAmelCase_     						=				{'''vocab_file''': '''spiece.model'''}

lowerCAmelCase_     						=				{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}

lowerCAmelCase_     						=				{
    '''google/pegasus-xsum''': 5_1_2,
}


lowerCAmelCase_     						=				logging.get_logger(__name__)

class 	_snake_case(			UpperCAmelCase      ):
 __snake_case:							Optional[Any]						=					VOCAB_FILES_NAMES

 __snake_case:							int						=					VOCAB_FILES_NAMES
 __snake_case:							List[str]						=					PRETRAINED_VOCAB_FILES_MAP
 __snake_case:							List[str]						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __snake_case:							Tuple						=					['''input_ids''', '''attention_mask''']






 def __init__(self       :  Optional[int]    ,		a       :  str    ,		a       :  Union[str, Any]="<pad>"    ,		a       :  List[str]="</s>"    ,		a       :  Optional[Any]="<unk>"    ,		a       :  int="<mask_2>"    ,		a       :  Union[str, Any]="<mask_1>"    ,		a       :  Union[str, Any]=None    ,		a       :  Union[str, Any]=1_03    ,		a       :  Optional[Dict[str, Any]] = None    ,		**a       :  Tuple    ,		)       ->   None:






    """simple docstring"""


    A__       =     offset
    if additional_special_tokens is not None:
       if not isinstance(a    ,		a    ):
          raise TypeError(
              f"""additional_special_tokens should be of type {type(a    )}, but is"""
              f""" {type(a    )}"""    )

       A__       =     (
           ([mask_token_sent] + additional_special_tokens)
           if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
           else additional_special_tokens
       )
       # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
       additional_special_tokens_extended += [
           f"""<unk_{i}>""" for i in range(len(a    )    ,		self.offset - 1    )
       ]

       if len(set(a    )    ) != len(a    ):
          raise ValueError(
              'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
              f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""    )
       A__       =     additional_special_tokens_extended
    else:
       A__       =     [mask_token_sent] if mask_token_sent is not None else []
       additional_special_tokens += [f"""<unk_{i}>""" for i in range(2    ,		self.offset    )]

    A__       =     {} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        eos_token=a    ,		unk_token=a    ,		mask_token=a    ,		pad_token=a    ,		mask_token_sent=a    ,		offset=a    ,		additional_special_tokens=a    ,		sp_model_kwargs=self.sp_model_kwargs    ,		**a    ,		)
    A__       =     mask_token_sent
    A__       =     vocab_file
    A__       =     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
    self.sp_model.Load(a    )

    # add special tokens to encoder dict
    A__       =     {
        0: self.pad_token,
        1: self.eos_token,
    }

    if self.mask_token_sent is not None:
       self.encoder.update(
           {
               2: self.mask_token_sent,
               3: self.mask_token,
           }    )

    if self.offset > 0:
       # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
       # mask_token_sent is already added to list -> so start at 1
       self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1    ,		self.offset - 1    )}    )

    A__       =     {v: k for k, v in self.encoder.items()}






 @property
 def 						_UpperCamelCase (self       :  List[Any]    )       ->   int:






    """simple docstring"""


    return len(self.sp_model    ) + self.offset






 def 						_UpperCamelCase (self       :  Optional[int]    )       ->   Dict[str, int]:






    """simple docstring"""


    A__       =     {self.convert_ids_to_tokens(a    ): i for i in range(self.vocab_size    )}
    vocab.update(self.added_tokens_encoder    )
    return vocab






 def __getstate__(self       :  Any    )       ->   Optional[int]:






    """simple docstring"""


    A__       =     self.__dict__.copy()
    A__       =     None
    return state






 def __setstate__(self       :  Dict    ,		a       :  int    )       ->   Any:






    """simple docstring"""


    A__       =     d

    # for backward compatibility
    if not hasattr(self    ,		'sp_model_kwargs'    ):
       A__       =     {}

    A__       =     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
    self.sp_model.Load(self.vocab_file    )






 def 						_UpperCamelCase (self       :  Dict    ,		a       :  str    )       ->   List[str]:






    """simple docstring"""


    return self.sp_model.encode(a    ,		out_type=a    )






 def 						_UpperCamelCase (self       :  List[Any]    ,		a       :  str    )       ->   int:






    """simple docstring"""


    if token in self.decoder:
       return self.decoder[token]
    elif token in self.added_tokens_decoder:
       return self.added_tokens_decoder[token]
    A__       =     self.sp_model.piece_to_id(a    )
    return sp_id + self.offset






 def 						_UpperCamelCase (self       :  Dict    ,		a       :  int    )       ->   str:






    """simple docstring"""


    if index in self.encoder:
       return self.encoder[index]
    elif index in self.added_tokens_encoder:
       return self.added_tokens_encoder[index]
    else:
       A__       =     self.sp_model.IdToPiece(index - self.offset    )
    return token






 def 						_UpperCamelCase (self       :  Tuple    ,		a       :  Optional[Any]    )       ->   int:






    """simple docstring"""


    A__       =     []
    A__       =     ''
    for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
          out_string += self.sp_model.decode(a    ) + token
          A__       =     []
       else:
          current_sub_tokens.append(a    )
    out_string += self.sp_model.decode(a    )
    return out_string.strip()






 def 						_UpperCamelCase (self       :  str    ,		a       :  str=False    )       ->   Any:






    """simple docstring"""


    return 1






 def 						_UpperCamelCase (self       :  Any    ,		a       :  Any    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     set(self.all_special_ids    )  # call it once instead of inside list comp
    all_special_ids.remove(self.unk_token_id    )  # <unk> is only sometimes special

    return [1 if x in all_special_ids else 0 for x in seq]






 def 						_UpperCamelCase (self       :  str    ,		a       :  List    ,		a       :  Optional[List] = None    ,		a       :  bool = False    )       ->   List[int]:






    """simple docstring"""


    if already_has_special_tokens:
       return self._special_token_mask(a    )
    elif token_ids_a is None:
       return self._special_token_mask(a    ) + [1]
    else:
       return self._special_token_mask(token_ids_a + token_ids_a    ) + [1]






 def 						_UpperCamelCase (self       :  int    ,		a       :  int    ,		a       :  int=None    )       ->   List[int]:






    """simple docstring"""


    if token_ids_a is None:
       return token_ids_a + [self.eos_token_id]
    # We don't expect to process pairs, but leave the pair logic for API consistency
    return token_ids_a + token_ids_a + [self.eos_token_id]






 def 						_UpperCamelCase (self       :  Union[str, Any]    ,		a       :  str    ,		a       :  Optional[str] = None    )       ->   Tuple[str]:






    """simple docstring"""


    if not os.path.isdir(a    ):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""    )
       return
    A__       =     os.path.join(
        a    ,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

    if os.path.abspath(self.vocab_file    ) != os.path.abspath(a    ) and os.path.isfile(self.vocab_file    ):
       copyfile(self.vocab_file    ,		a    )
    elif not os.path.isfile(self.vocab_file    ):
       with open(a    ,		'wb'    ) as fi:
          A__       =     self.sp_model.serialized_model_proto()
          fi.write(a    )

    return (out_vocab_file,)








'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_     						=				logging.get_logger(__name__)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase=False						):






   '''simple docstring'''







   A__       =     []
   for i in range(config.num_hidden_layers						):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""deit.encoder.layer.{i}.layernorm_before.weight""")						)
      rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""deit.encoder.layer.{i}.layernorm_before.bias""")						)
      rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""deit.encoder.layer.{i}.attention.output.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""deit.encoder.layer.{i}.attention.output.dense.bias""")						)
      rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""deit.encoder.layer.{i}.layernorm_after.weight""")						)
      rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""deit.encoder.layer.{i}.layernorm_after.bias""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""deit.encoder.layer.{i}.intermediate.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""deit.encoder.layer.{i}.intermediate.dense.bias""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""deit.encoder.layer.{i}.output.dense.weight""")						)
      rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""deit.encoder.layer.{i}.output.dense.bias""")						)

   # projection layer + position embeddings
   rename_keys.extend(
       [
           ('cls_token', 'deit.embeddings.cls_token'),
           ('dist_token', 'deit.embeddings.distillation_token'),
           ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
           ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
           ('pos_embed', 'deit.embeddings.position_embeddings'),
       ]						)

   if base_model:
      # layernorm + pooler
      rename_keys.extend(
          [
              ('norm.weight', 'layernorm.weight'),
              ('norm.bias', 'layernorm.bias'),
              ('pre_logits.fc.weight', 'pooler.dense.weight'),
              ('pre_logits.fc.bias', 'pooler.dense.bias'),
          ]						)

      # if just the base model, we should remove "deit" from all keys that start with "deit"
      A__       =     [(pair[0], pair[1][4:]) if pair[1].startswith('deit'						) else pair for pair in rename_keys]
   else:
      # layernorm + classification heads
      rename_keys.extend(
          [
              ('norm.weight', 'deit.layernorm.weight'),
              ('norm.bias', 'deit.layernorm.bias'),
              ('head.weight', 'cls_classifier.weight'),
              ('head.bias', 'cls_classifier.bias'),
              ('head_dist.weight', 'distillation_classifier.weight'),
              ('head_dist.bias', 'distillation_classifier.bias'),
          ]						)

   return rename_keys







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=False						):






   '''simple docstring'''







   for i in range(config.num_hidden_layers						):
      if base_model:
         A__       =     ''
      else:
         A__       =     'deit.'
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      A__       =     state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""						)
      A__       =     state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""						)
      # next, add query, keys and values (in that order) to the state dict
      A__       =     in_proj_weight[
          : config.hidden_size, :
      ]
      A__       =     in_proj_bias[: config.hidden_size]
      A__       =     in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      A__       =     in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      A__       =     in_proj_weight[
          -config.hidden_size :, :
      ]
      A__       =     in_proj_bias[-config.hidden_size :]







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     dct.pop(UpperCAmelCase						)
   A__       =     val







def 						_A      ( ):






   '''simple docstring'''







   A__       =     'http://images.cocodataset.org/val2017/000000039769.jpg'
   A__       =     Image.open(requests.get(UpperCAmelCase      ,stream=UpperCAmelCase						).raw						)
   return im







@torch.no_grad()
def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     DeiTConfig()
   # all deit models have fine-tuned heads
   A__       =     False
   # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
   A__       =     1000
   A__       =     'huggingface/label-files'
   A__       =     'imagenet-1k-id2label.json'
   A__       =     json.load(open(hf_hub_download(UpperCAmelCase      ,UpperCAmelCase      ,repo_type='dataset'						)      ,'r'						)						)
   A__       =     {int(UpperCAmelCase						): v for k, v in idalabel.items()}
   A__       =     idalabel
   A__       =     {v: k for k, v in idalabel.items()}
   A__       =     int(deit_name[-6:-4]						)
   A__       =     int(deit_name[-3:]						)
   # size of the architecture
   if deit_name[9:].startswith('tiny'						):
      A__       =     192
      A__       =     768
      A__       =     12
      A__       =     3
   elif deit_name[9:].startswith('small'						):
      A__       =     384
      A__       =     1536
      A__       =     12
      A__       =     6
   if deit_name[9:].startswith('base'						):
      pass
   elif deit_name[4:].startswith('large'						):
      A__       =     1024
      A__       =     4096
      A__       =     24
      A__       =     16

   # load original model from timm
   A__       =     timm.create_model(UpperCAmelCase      ,pretrained=UpperCAmelCase						)
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   A__       =     timm_model.state_dict()
   A__       =     create_rename_keys(UpperCAmelCase      ,UpperCAmelCase						)
   for src, dest in rename_keys:
      rename_key(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)
   read_in_q_k_v(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)

   # load HuggingFace model
   A__       =     DeiTForImageClassificationWithTeacher(UpperCAmelCase						).eval()
   model.load_state_dict(UpperCAmelCase						)

   # Check outputs on an image, prepared by DeiTImageProcessor
   A__       =     int(
       (256 / 224) * config.image_size						)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
   A__       =     DeiTImageProcessor(size=UpperCAmelCase      ,crop_size=config.image_size						)
   A__       =     image_processor(images=prepare_img()      ,return_tensors='pt'						)
   A__       =     encoding['pixel_values']
   A__       =     model(UpperCAmelCase						)

   A__       =     timm_model(UpperCAmelCase						)
   assert timm_logits.shape == outputs.logits.shape
   assert torch.allclose(UpperCAmelCase      ,outputs.logits      ,atol=1e-3						)

   Path(UpperCAmelCase						).mkdir(exist_ok=UpperCAmelCase						)
   print(F"""Saving model {deit_name} to {pytorch_dump_folder_path}"""						)
   model.save_pretrained(UpperCAmelCase						)
   print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
   image_processor.save_pretrained(UpperCAmelCase						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--deit_name''',
        default='''vit_deit_base_distilled_patch16_224''',
        type=str,
        help='''Name of the DeiT timm model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
    )

    lowerCAmelCase_     						=				parser.parse_args()
    convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)


